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Description

To implement Bayes’ Theorem for data assimilation, an ensemble Kalman filter (EnKF) uses a set of model integrations to simulate the temporally-varying background probability distribution function. Due to the merit of derived data assimilation solution coherently combining model and observational information, EnKF has risen as a widely-promising data assimilation algorithm in weather and climate studies. However, its huge computational resource demanding for ensemble model integrations sets a significant limitation on applications in high resolution coupled earth systems. Given that the background error statistics consist of stationary, slow-varying and fast varying parts, a high efficiency approximate EnKF (Hea-EnKF) is designed to dramatically enhance the computational efficiency. The Hea-EnKF is a combination of stationary, slow-varying and fast-varying filters, implemented in regressions sampled from large size single model solution data and updated with the model integrations. Validation shows that due to improved representation on stationary and slow-varying background statistics, the Hea-EnKF while only requiring a small fraction of computer resources can be better than the standard EnKF that uses finite ensemble statistics. The new algorithm makes practical to assimilate multi-source observations into any high-resolution coupled model intractable with current super-computing power for weather-climate analysis and predictions.